05

AI news today

Page 4 of 12

These are earlier cards, kept as text. Pictures and short summaries stay only while a card is on the first page; the link goes to the original either way.

Source OpenAI
Asana cuts model costs 76x in browser tests with GPT-6.1 Sol

Oct 9

What we saw

“Using GPT-6 Astra in Codex, Asana made its browser agent 76x cheaper and 5x faster in tests to offer customers more capable mod…”

They claim — Using GPT-6 Astra in Codex, Asana made its browser agent 76x cheaper and 5x faster in tests to offer customers more capable models.

Published 2026-10-09

Fetched 2026-10-09 23:01 UTC · 0f1eab57236c05e2

Torn from — the listing only (the original would not open)

Source OpenAI
Sophos cuts threat investigation time by 96% with OpenAI Daybreak

Oct 9

What we saw

“Discover how Sophos uses OpenAI’s Daybreak to cut cyber-threat investigation time by 96% and automate 52% of MDR cases while …”

They claim — Discover how Sophos uses OpenAI’s Daybreak to cut cyber-threat investigation time by 96% and automate 52% of MDR cases while preserving human oversight.

Published 2026-10-09

Fetched 2026-10-09 23:01 UTC · c2bd9140cf451f0f

Torn from — the listing only (the original would not open)

text-to-imagediffusersSource Hugging Face
Qwen/Qwen-Image-2.1-Turbo

536 likes4.7k downloads

otherbuilt on Qwen-Image-2.1

What we saw

This source gives a title and a link, nothing to quote.

They claim — The text-to-image example uses 1680 × 2512 portrait resolution; the editing example uses 2048 × 2048.

Published 2026-10-09

Fetched 2026-10-09 23:01 UTC · 7cad2807a6dba4a4

Torn from — the model card

Source OpenAI
LegalOn halves Codex costs while maintaining development speed

Oct 8

What we saw

“LegalOn cut estimated daily Codex costs by 65% while maintaining development speed. It matched Astra, Sol, and Luna to tasks an…”

They claim — LegalOn cut estimated daily Codex costs by 65% while maintaining development speed.

Published 2026-10-08

Fetched 2026-10-09 23:01 UTC · a13e16f89e5be705

Torn from — the listing only (the original would not open)

JavaScriptSource GitHub
wheresryan22/anatomy

3.1k stars238 forks

MITJavaScriptpushed today

Location, estimated · Not stated

What we saw

“A Claude Code skill that turns a technical idea into a detailed, interactive isometric figure of a real machine. It makes SVG w…”

Published 2026-10-08

Fetched 2026-10-11 23:01 UTC · 30a09c855d09c4c2

What the account wrote — The account left it blank

Torn from — the README

GoSource GitHub
mhtsec/ARTEX

3.1k stars5.5k forks

AGPL-3.0Gopushed yesterday

Location, estimated · Not stated

What we saw

“AI 自主渗透测试系统 | 百度“agent+”攻防挑战赛冠军项目”

They claim — 点「更新」后:下载当前平台的发布包 → 比对 Release 的 SHA256SUMS → 用 -h 冒烟测试新二进制 → 暂存为 artex.

Published 2026-10-08

Fetched 2026-10-09 23:01 UTC · 0cc27db3d1a52caa

What the account wrote — The account left it blank

Torn from — the README

text-generationggufSource Hugging Face
ConwayResearch/Underdog-Saluki-27B-1.0

320 likes109k downloads

apache-2.0built on Qwen3.8-27B-GSQ-RCO-GGUF

What we saw

This source gives a title and a link, nothing to quote.

They claim — Qwen3.8-27B in under 8 GB, tuned to keep tool calling intact.

Published 2026-10-08

Fetched 2026-10-09 23:01 UTC · fd535ff0fdcb55b5

Torn from — the model card

text-classificationtransformersSource Hugging Face
Phocinae/Phocinae-Largha-150M-v1

227 likes89 downloads

apache-2.0built on mmBERT-small

What we saw

This source gives a title and a link, nothing to quote.

They claim — 55.0% fewer LLM calls, 21.0 ms per decision on an RTX 5090.

Published 2026-10-08

Fetched 2026-10-09 23:01 UTC · 53fb1f4f0b1a1150

Torn from — the model card

Source HF Daily Papers
DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training

37 upvotes2 comments

9 authorsno limits stated

What we saw

“We present DreamTrue, a multi-view, cross-embodiment robot world model for action-faithful and physically plausible video predi…”

They claim — On AgiBot, DreamTrue attains state-of-the-art action following, while reducing the human-assessed interaction defect rate from from 48.12% to 6.25%.

Published 2026-10-07

Fetched 2026-10-09 23:01 UTC · 074b3efb8c207b56

Torn from — the abstract

Thread ↗
Source HF Daily Papers
OuroWorld: Bringing Any 3D World Alive as Diverse, Endlessly Looping 3D Cinemagraphs

42 upvotes2 comments

6 authorsno limits stated

What we saw

“Recent 3D world models generate photorealistic, explorable scenes that remain frozen in time. OuroWorld is a mask-free framewor…”

They claim — On 39 reconstructed and generated scenes, OuroWorld outperforms all baselines and wins 70.8%-99.0% of user-study comparisons.

Published 2026-10-07

Fetched 2026-10-09 23:01 UTC · 2df83b14d4e56c3d

Torn from — the abstract

Thread ↗
Source HF Daily Papers
Foundations of Large Language Models

42 upvotes3 comments

2 authorsno limits stated

What we saw

“This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather tha…”

Published 2026-10-07

Fetched 2026-10-09 23:01 UTC · 5a28cbe0f0dba767

Torn from — the abstract

Thread ↗
Source HF Daily Papers
Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks

35 upvotes5 comments

8 authorsno limits stated

What we saw

“Learning to act in unfamiliar environments requires agents to infer how the world works and revise that understanding as new ev…”

They claim — On ARC-AGI-3, the single-model agent clears every level of all 25 public games, achieves a mean Relative Human Action Efficiency (RHAE) of 100.0, and uses 44% of the human action count.

Published 2026-10-07

Fetched 2026-10-09 23:01 UTC · 2ec664cc3165cff7

Torn from — the abstract

Thread ↗
Source HF Daily Papers
MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement

74 upvotes2 comments

149 authorsno limits stated

What we saw

“Reinforcement learning (RL) is the central training paradigm for advancing large foundation models towards self-improvement. Th…”

They claim — We scale RL compute along three dimensions: (1) larger batches and higher throughput, with an asynchronous training that consumes 1,568 samples and 2.7-3.7B tokens per step at context len…

Published 2026-10-07

Fetched 2026-10-09 23:01 UTC · 1b5db0f1008c3cfe

Torn from — the abstract

Thread ↗
Source HF Daily Papers
TokenRouter: Efficient Serving System for Token-Level LLM Routing

129 upvotes2 comments

7 authorsno limits stated

What we saw

“Large language model (LLM) routing distributes inference work across different models, advancing the cost-quality Pareto fronti…”

They claim — Across diverse routing algorithms, workloads, and model pairs, TokenRouter achieves 2.01-64.15x higher decoding throughput than existing systems, substantially advancing the serving effic…

Published 2026-10-07

Fetched 2026-10-09 23:01 UTC · e1c4e3f59be4b797

Torn from — the abstract

Thread ↗
Source HF Daily Papers
REMORY: Learning Residual Memory for Context Compaction

38 upvotes5 comments

8 authorsno limits stated

What we saw

“Long-horizon agents compact their history to continue within a finite context window, but a textual summary alone may not suppo…”

They claim — On SummHay, REMORY improves source attribution at nearly unchanged insight coverage and approaches the full-context joint score using only 5.2% of the input positions.

Published 2026-10-07

Fetched 2026-10-09 23:01 UTC · d6956d1b7ab9450f

Torn from — the abstract

Thread ↗
Source HF Daily Papers
Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments?

136 upvotes3 comments

7 authorsno limits stated

What we saw

“Learning from experience is essential for LLM agents to adapt to unfamiliar and dynmaic environments. Evaluating this ability i…”

They claim — (2) Human agent gap: Top-performing human players achieve higher peak scores than the evaluated agents.

Published 2026-10-07

Fetched 2026-10-09 23:01 UTC · 2a9358bd3d19b21d

Torn from — the abstract

Thread ↗
Source Google AI
Introducing Playground: Create and play custom games

Oct 7

no limits stated3.0k words

What we saw

“Playground is a new experimental gaming platform that lets you create, play, and share custom games.”

They claim — @keyframes gemini-draw-border-timeline{0%{--border-draw-percent:0%;--border-mask-color:transparent;--btn-bg-color:var(--btn-bg-start);--gradient-angle:var(--start-angle,145deg)}85%{--bord…

Published 2026-10-07

Fetched 2026-10-09 23:01 UTC · 6f521205a43d3a67

Torn from — the page itself

PythonaiSource GitHub
LosaLosSantos/aurelio-finance

2.2k stars286 forks

MITPythonpushed today

Location, estimated · Not stated

What we saw

“Open-source personal finance app with an AI financial advisor: track your net worth, investments, ETFs, cash and debts on your …”

Published 2026-10-06

Fetched 2026-10-09 23:01 UTC · 984d32f396b5debc

What the account wrote — The account left it blank

Torn from — the README

PythonSource GitHub
zhongerxin/iPhone-use

3.0k stars303 forks

MITPythonpushed today

Location, estimated · Not stated

What we saw

“让 Codex 通过 USB 操作真实 iPhone:引导安装、App 自动化、实时屏幕与截图回退。”

Published 2026-10-06

Fetched 2026-10-09 23:01 UTC · bdb641bc7307d99d

What the account wrote — The account left it blank

Torn from — the README

image-text-to-texttransformersSource Hugging Face
LiquidAI/d1-omni-600M

106 likes10k downloads

otherbuilt on LFM2.5-Encoder-350M

What we saw

This source gives a title and a link, nothing to quote.

They claim — top answer on 0.8% of text and 1.7% of audio rows.

Published 2026-10-05

Fetched 2026-10-11 23:01 UTC · c0297536843caeee

Torn from — the model card

text-to-imagepytorchSource Hugging Face
speridlabs/iris-3b

148 likes393 downloads

apache-2.0

What we saw

This source gives a title and a link, nothing to quote.

They claim — Restoration tiles large outputs into overlapping 1024×1024 windows.

Published 2026-10-05

Fetched 2026-10-10 23:01 UTC · fa4451995d49a027

Torn from — the model card

text-classificationpytorchSource Hugging Face
perplexity-ai/pplx-decider-v1.1-27b

121 likes2.2k downloads

apache-2.0built on Qwen3.8-27B

What we saw

This source gives a title and a link, nothing to quote.

They claim — model, that boost the Decision Index score from 56.4 to 61.56, now outperforming Jev by more than 3.5 points, while using the same backbone.

Published 2026-10-05

Fetched 2026-10-10 23:01 UTC · 2db5bbc68088a7bf

Torn from — the model card

image-text-to-textsafetensorsSource Hugging Face
nerkyor/Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2

233 likes57k downloads

apache-2.0built on Qwen3.8-27B-EfficientThink-Uncensored-K3-Opus5-Grok4.6-GPT5.6Sol-SFT-SimPO-DFlash2

What we saw

This source gives a title and a link, nothing to quote.

They claim — For NInfer W4A4, the 2 LCB empty answers are problems 11 and 15, both truncated with no code; the 2 GPQA empty answers are problems 79 and 127, both truncated with an empty pred.

Published 2026-10-04

Fetched 2026-10-09 23:01 UTC · 1c8c0b72263dc3b9

Torn from — the model card

Source Simon Willison
Quoting Muse AI Agent

Sep 28

no limits stated134 words

What we saw

“Bad news on the MX Keys Mini pickup. Usman showed up at your building around 9:15 and waited, messaged a bunch of times, and no…”

Published 2026-09-28

Fetched 2026-09-28 05:05 UTC · e576682f7f3370bc

Torn from — the listing only (the original would not open)

Papers, repos, models, blogs, Reddit and podcasts — once a day, by machine. Everything collected gets taken apart once — the README, the abstract, the model card — and what that turns up is kept for good, whether or not it becomes a card. Titles and links go to the original; nothing is reproduced here.

The grey line on each card is what the teardown found, not what the source advertises: the licence, the last commit, the model it was built on, whether the paper states its own limits. Open “What we saw” to check what was read to get it. Where it says listing only, the original could not be read — some sites refuse us, and we do not pretend otherwise.

A line in quotation marks is a sentence lifted from the source itself — their number, not a measurement of ours. We copy it and leave it unchecked; that is the point of quoting it.

Topics are assigned by matching words in the original title, quote and tags against a fixed list — no model reads the card. Some cards match nothing and carry no topic at all; pick a topic and those drop out. Source and topic narrow together.

Each card keeps the sentence we actually read at the moment we read it, plus a fingerprint of the response we received. That sentence — not the whole page — is what we can still stand behind if the original later changes or disappears.

Guessed from one free-text field the account filled in itself, and half of them leave it empty. Shown as its own value rather than hidden.